






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch
@is_pipeline_test
class 				A_     (    unittest.TestCase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 _SCREAMING_SNAKE_CASE				:      Dict			=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


 def 			snake_case__					(						self				,	_A				,	_A				,	_A)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   List[str]      	=					TextaTextGenerationPipeline(model=snake_case__				,	tokenizer=snake_case__)
     return generator, ["Something to write", "Something else"]


 def 			snake_case__					(						self				,	_A				,	_A)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   int      	=					generator('''Something there''')
     self.assertEqual(snake_case__				,	[{'''generated_text''': ANY(snake_case__)}])
     # These are encoder decoder, they don't just append to incoming string
     self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''))

     _UpperCAmelCase					:   Union[str, Any]      	=					generator(['''This is great !''', '''Something else''']				,	num_return_sequences=2				,	do_sample=snake_case__)
     self.assertEqual(
         snake_case__				,	[
             [{'''generated_text''': ANY(snake_case__)}, {'''generated_text''': ANY(snake_case__)}],
             [{'''generated_text''': ANY(snake_case__)}, {'''generated_text''': ANY(snake_case__)}],
         ]				,	)

     _UpperCAmelCase					:   Union[str, Any]      	=					generator(
         ['''This is great !''', '''Something else''']				,	num_return_sequences=2				,	batch_size=2				,	do_sample=snake_case__)
     self.assertEqual(
         snake_case__				,	[
             [{'''generated_text''': ANY(snake_case__)}, {'''generated_text''': ANY(snake_case__)}],
             [{'''generated_text''': ANY(snake_case__)}, {'''generated_text''': ANY(snake_case__)}],
         ]				,	)

     with self.assertRaises(snake_case__):
         generator(4)


 @require_torch
 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					pipeline('''text2text-generation'''				,	model='''patrickvonplaten/t5-tiny-random'''				,	framework='''pt''')
     # do_sample=False necessary for reproducibility
     _UpperCAmelCase					:   Optional[int]      	=					generator('''Something there'''				,	do_sample=snake_case__)
     self.assertEqual(snake_case__				,	[{'''generated_text''': ''''''}])

     _UpperCAmelCase					:   List[str]      	=					3
     _UpperCAmelCase					:   Any      	=					generator(
         '''Something there'''				,	num_return_sequences=snake_case__				,	num_beams=snake_case__				,	)
     _UpperCAmelCase					:   Optional[int]      	=					[
         {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
         {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
         {'''generated_text''': ''''''},
     ]
     self.assertEqual(snake_case__				,	snake_case__)

     _UpperCAmelCase					:   Dict      	=					generator('''This is a test'''				,	do_sample=snake_case__				,	num_return_sequences=2				,	return_tensors=snake_case__)
     self.assertEqual(
         snake_case__				,	[
             {'''generated_token_ids''': ANY(torch.Tensor)},
             {'''generated_token_ids''': ANY(torch.Tensor)},
         ]				,	)
     _UpperCAmelCase					:   Dict      	=					generator.model.config.eos_token_id
     _UpperCAmelCase					:   Union[str, Any]      	=					'''<pad>'''
     _UpperCAmelCase					:   int      	=					generator(
         ['''This is a test''', '''This is a second test''']				,	do_sample=snake_case__				,	num_return_sequences=2				,	batch_size=2				,	return_tensors=snake_case__				,	)
     self.assertEqual(
         snake_case__				,	[
             [
                 {'''generated_token_ids''': ANY(torch.Tensor)},
                 {'''generated_token_ids''': ANY(torch.Tensor)},
             ],
             [
                 {'''generated_token_ids''': ANY(torch.Tensor)},
                 {'''generated_token_ids''': ANY(torch.Tensor)},
             ],
         ]				,	)


 @require_tf
 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					pipeline('''text2text-generation'''				,	model='''patrickvonplaten/t5-tiny-random'''				,	framework='''tf''')
     # do_sample=False necessary for reproducibility
     _UpperCAmelCase					:   Optional[int]      	=					generator('''Something there'''				,	do_sample=snake_case__)
     self.assertEqual(snake_case__				,	[{'''generated_text''': ''''''}])





def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()